Details

Autor(en) / Beteiligte

• Mueller, Franziska
• Davis, Micah
• Bernard, Florian
• Sotnychenko, Oleksandr
• Verschoor, Mickeal
• Otaduy, Miguel A.
• Casas, Dan
• Theobalt, Christian

Titel

Real-time pose and shape reconstruction of two interacting hands with a single depth camera

Ist Teil von

• ACM transactions on graphics, 2019-08, Vol.38 (4), p.1-13

Erscheinungsjahr

2019

Quelle

ACM Digital Library

Beschreibungen/Notizen

• We present a novel method for real-time pose and shape reconstruction of two strongly interacting hands. Our approach is the first two-hand tracking solution that combines an extensive list of favorable properties, namely it is marker-less, uses a single consumer-level depth camera, runs in real time, handles inter- and intra-hand collisions, and automatically adjusts to the user's hand shape. In order to achieve this, we embed a recent parametric hand pose and shape model and a dense correspondence predictor based on a deep neural network into a suitable energy minimization framework. For training the correspondence prediction network, we synthesize a two-hand dataset based on physical simulations that includes both hand pose and shape annotations while at the same time avoiding inter-hand penetrations. To achieve real-time rates, we phrase the model fitting in terms of a nonlinear least-squares problem so that the energy can be optimized based on a highly efficient GPU-based Gauss-Newton optimizer. We show state-of-the-art results in scenes that exceed the complexity level demonstrated by previous work, including tight two-hand grasps, significant inter-hand occlusions, and gesture interaction. 1